Abstract
This paper describes four recently developed methods dealing with decision making under uncertainty in two considered directions (maximizing of a utility function and getting a more reliable global description of considered situation based on observed data). Both cases face the problem, that the data are unreliable, since they contain uncertainty caused by the sources.
For the former direction, a game theory reformulation of the decision making task brings a smoother way to reach an optimal choice. If the latter direction is considered, a merging procedure (also called fusion) processing the data should help us.
Beside the methods, this paper describes also one tool used for comparison of the fusion algorithms.